Multi-variable product rank

Abstract

Methods, systems, and computer-readable media for ranking products using multiple data sources are provided. A computerized ranking system includes a ranking engine, loaders, and a presentation component. The ranking engine calculates a score for each product based on multiple counts logged by data sources. Loaders communicatively connected to the ranking engine provide the counts to the data sources. The presentation component generates a ranked product list for display on client devices in response to requests for a list of popular products.

1. One or more computer-readable media storing computer-usable instructions that cause one or more processors to perform a method that ranks products, the method comprising:
 retrieving counts associated with each product from disparate data sources; 
 normalizing counts based on all products included in the database; and 
 assigning a rank to each product based on a score calculated from the normalized counts, wherein the score is calculated by summing the normalized counts in accordance with the following: Score=αP+βC+δR+ζS, where P is a normalized number for page view for each product, C is a normalized number of clicks, R is a normalized revenue, S is a number of appearances in search results, α is a weighting factor for P, β is a weighting factor for C, δ is a weighting factor for R, and ζ; is a weighting factor for S. 
 
     
     
       2. The media of  claim 1 , further comprising: generating for display a list of products based on the assigned rank. 
     
     
       3. The media of  claim 2 , further comprising: formatting the list of products as one of a HTML list, a XML list, or a RSS list. 
     
     
       4. A computer-implemented method to rank products, the method comprising:
 receiving multiple counts for products from a plurality of data sources; 
 normalizing, by a processor of a computer, the counts for each product within each data source; 
 assigning, by a processor of a computer, a weight to each data source, wherein weight is used to calculate the score; 
 summing, by a processor of a computer, the normalized and weighted counts to calculate a score for each product, wherein the score is calculated using the following: Score=αP+βC+δR+ζS, where P is a normalized number for page view for each product, C is a normalized number of clicks, R is a normalized revenue, S is a number of appearances in search results, α is a weighting factor for P, β is a weighting factor for C, δ is a weighting factor for R, and ζ; is a weighting factor for S; and 
 generating, by a processor of a computer, a list based on the calculated score for each product. 
 
     
     
       5. The method of  claim 4 , wherein the counts are periodically received from the data sources. 
     
     
       6. The method of  claim 4 , wherein the product's rank across discrete ranges of time is comparable based on the normalizations applied to counts. 
     
     
       7. The method of  claim 4 , wherein the normalized counts range from 0 to 1. 
     
     
       8. The method of  claim 4 , wherein the list includes categories and the ranks for products in each category are normalized. 
     
     
       9. A computerized ranking system, the ranking system comprising:
 a computer processor coupled to a memory, wherein the computer processor is programmed to execute: 
 a ranking engine to calculate a score for each product stored in product databases, wherein the score is based on multiple counts logged by a plurality of data sources and is derived by summing normalized multiple counts in accordance with the following: Score=αP+βC+δR+ζS, where P is a normalized number for page view for each product, C is a normalized number of clicks, R is a normalized revenue, S is a number of appearances in search results, α is a weighting factor for P, β is a weighting factor for C, δ is a weighting factor for R, and ζ; is a weighting factor for S; 
 a plurality of loaders communicatively connected to the ranking engine, the loaders receive the counts for each product from the plurality of data sources; and 
 a presentation component to format a list of products, based on the scores calculated by the ranking engine, for display on client devices in response to requests for a list of popular products, wherein the display includes a graphical summary of score differences for the list of products over a period of time. 
 
     
     
       10. The computerized ranking system of  claim 9 , wherein the score is based on normalized counts logged by the plurality of data sources. 
     
     
       11. The computerized ranking system of  claim 9 , wherein the counts include number of page views, number of clicks, amount of revenue, number of entries in a search log. 
     
     
       12. The computerized ranking system of  claim 9 , wherein the counts include offline store transaction data. 
     
     
       13. The computerized ranking system of  claim 9 , wherein the counts include number of sales generates, number of seconds a user dwells on a product displayed on the computer. 
     
     
       14. The computerized ranking system of  claim 9 , wherein the loader is a device that accesses a data source to obtain the counts. 
     
     
       15. The computerized ranking system of  claim 9 , wherein the data sources are relational databases that store counts. 
     
     
       16. The computerized ranking system of  claim 9 , wherein the loaders periodically retrieves the counts from the multiple data source. 
     
     
       17. The computerized ranking system of  claim 9 , wherein each data source is associated with a specific loader. 
     
     
       18. The computerized ranking system of  claim 9 , wherein the ranking engine normalizes the scores based on categories selected by a user of client device to rank products based on score within the selected category. 
     
     
       19. The computerized ranking system of  claim 9 , wherein the presentation component is configured to output a ranked list in one of HTML format, RSS format, or XML format.